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Review Aspect Multiclass Brier Score Calculator
x402
Review Aspect Multiclass Brier Score Calculator is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-14).
Computes the multiclass Brier score to evaluate probabilistic classifier quality on review aspect labels (performance, appearance, value, other)
Price / call$0.01
Success—
StatusUnknown
How this service works
Review aspect - Multiclass Brier score: Multiclass Brier score for Review aspect. Up to 200 supplied labelled text records. Local dataset tools, evaluation and small naive-Bayes baselines; no hosted model or foundation-model training. One of 25 profiles or calculations across 20 shared operations in this sector. 0.01 USDC includes one bounded multiclass brier score deliverable.
Output
A bounded multiclass Brier score value (0 to 2, lower is better) summarizing how well the classifier's predicted probability distributions match the true one-hot label vectors across up to 200 supplied labelled review-aspect records; includes one scalar deliverable per call.
Example use cases
E-commerce review classifier calibration check
“I've trained a classifier that tags product reviews as performance, appearance, value, or other, and I want to know how well its probability outputs are calibrated. Here are 80 labelled records with actual labels, predicted labels, and four-class probability vectors — compute the multiclass Brier score for me.”
A/B comparison of two aspect models
“I'm comparing two versions of my review aspect model and need Brier scores for both. Can you compute the multiclass Brier score for this first batch of 120 records with their actual labels and probability outputs across the performance, appearance, value, and other categories?”
Naive-Bayes baseline evaluation for review aspects
“I've built a naive-Bayes baseline that classifies reviews into performance, appearance, value, or other, and I want to benchmark its probabilistic quality. Please calculate the multiclass Brier score using these 40 labelled records with actual classes and predicted probability distributions.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-ml-review-aspect-brier?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, low-cost probabilistic evaluation metric for a 4-class review aspect classifier using local dataset tools and a naive-Bayes baseline context. It is purpose-built for the specific review aspect taxonomy (performance, appearance, value, other) and costs only $0.01 USDC per call. Prefer it over generic ML APIs when you want a bounded Brier score computation without needing to train or host a foundation model.
Side effects
- Records array exceeds 200 items — request rejected
- Probability arrays not exactly length 4 — validation error
- Probabilities outside [0,1] range — validation error
- Missing required fields (id, text, actual, predicted, probabilities) — schema error
- Enum values not in {performance, appearance, value, other} — rejected
- Empty records array — no score computed
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array |
Tags
x402
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